Date of Award


Publication Type

Doctoral Thesis

Degree Name



Industrial and Manufacturing Systems Engineering

First Advisor

Waguih El Maraghy


ABC, Costing, Industry 4.0, Job-Shop, Mathematical Modelling, RMS




Manufacturing continues to face escalated cost challenges on a global scale. To gain a competitive advantage among their rivals, manufacturing firms continuously strive to lower their manufacturing costs than their competitors. This dissertation introduces mathematical optimization model based on an Activity-Based Costing (ABC) method, which considers the relationship between hourly rates and annual hours on each machine/workcentre. Several constraints are considered in the proposed models, such as the cost of reconfiguration, capacity, available machining hours, a decision on facility expansion and a cost-benefit analysis on industry 4.0 implementation. The model outputs are the optimum hourly rates, deciding which jobs to accept or reject, and determining reconfiguration's financial feasibility. Reconfiguration in this dissertation describes system-level reconfiguration (investing in additional equipment/machinery) and/or machine-level reconfiguration (extra module to a piece of existing equipment) as well as factory-level (in terms of expanding additional factory segments to the existing facility). The model will be applied to a real-life case study of a global original equipment manufacturer (OEM) of machinery. The mathematical models proposed in this dissertation are developed based on a multinational hydraulic-press manufacturing company. The company owns a local machine shop (one of the sister companies in North America) for building hydraulic presses meant to be delivered to companies producing engineered wood products (such as OSB (oriented Strand Board), PB (Particle Board), and MDF Board (Medium-Density Fibre) …etc.). The sister company in North America occupies a footprint of 5,000 meters squared with a number of capabilities such as machining (turning and machining centres, welding, assembly, material handling…etc.). Several aspects of the model proposed in this dissertation had been implemented in the company such as the bi-directional relationship between total hours and hourly rates which assisted the company in gaining more jobs and projects. In addition, connectivity between strategic suppliers and company branched has been established (enabler of Industry 4.0). The proposed model's novelty incorporates the bi-directional relationship between hourly rates and annual hours in each workcentre. It provides a managerial decision-making tool for the investment level required to pursue new business and gaining a competitive advantage over rivals. Furthermore, a cost-benefit analysis is performed on the implementation of Industry 4.0. The primary aspect considered in industry 4.0 is Information Communication Technology (ICT) infrastructure with strategic suppliers to intensify interconnection between the manufacturing firm and the strategic suppliers. This research's significance is focused on cost analysis and provides managers in manufacturing facilities with the required decision-making tools to decide on orders to accept or decline, as well as investing in additional production equipment, facility expansion, as well as Industry 4.0. In addition, this research will also help manufacturing companies achieve a competitive edge among rivals by reducing hourly rates within their facility. Furthermore, the implementation of the model reduced hourly rates for workcentres by up to 25% as a result of accepting more jobs (and accordingly, machining hours) on the available workcentres, and hence, reducing the hourly rates. This implementation has helped the company gain a competitive advantage among rivals since pricing of products submitted to customer was reduced. Additional benefits and significance are (1) providing manufacturing companies with a method to quantify the decision-making process for right-sizing their manufacturing space, (2) the ability to justify growing a scalable system (machine level, system-level and factory level) using costing (not customer demand), (3) expanding market share and, (4) reducing operational cost and allowing companies a numerical model to justify scaling the manufacturing system.